Standardize Spearman notation

app.py

@@ -1709,7 +1709,7 @@ def feature_spec(dataset: str) -> tuple[str, str, str, float | None]:
         return (
             "spearman_corr",
             "Drifting-gratings gOSI",
-            "Spearman’s ρ",
+            "Spearman’s r",
             0.0,
         )
     if dataset == "ratinabox":
@@ -2112,7 +2112,7 @@ def trial_retrain_figures() -> tuple[go.Figure, go.Figure, go.Figure, pd.DataFra
                 )
             relation.add_hline(y=0, line_dash="dash", line_color="#69737D")
             relation_text = (
-                f"Spearman ρ = {rho:.2f}; n = {len(relation_frame)}"
+                f"Spearman’s r = {rho:.2f}; n = {len(relation_frame)}"
                 if pd.notna(rho)
                 else f"n = {len(relation_frame)}; select at least two methods for correlation"
             )
@@ -3508,7 +3508,7 @@ def update_feature(dataset: str, models: list[str] | None):
     _column, _target, metric, _reference = feature_spec(dataset)
     if dataset == "allen_neuropixels":
         definition = (
-            "Spearman’s ρ measures association between feature-attribution values "
+            "Spearman’s r measures association between feature-attribution values "
             "and each unit’s gOSI measured from drifting gratings."
         )
     elif dataset == "ratinabox":

data/release_manifest.json

@@ -6,7 +6,7 @@
   "manuscript_working_version": "manuscript_v8",
   "schema_version": 1,
   "source": "paper/results, active consistency and feature-attribution artifacts, benchmark dataset arrays, and Figure 5 prediction sidecars",
-  "source_git_revision": "e8803f68d58da2e0ee43ad51782cb880731bb884",
+  "source_git_revision": "b22e5c3452975fd94c455ef16639bd95f993f9f7",
   "source_repository": "https://github.com/TangLab-UBC/behavior_benchmarking",
   "tables": {
     "clean_prediction_summary.csv": {